2 Search Results for "Menuhin, Boaz"

Document
DOI: 10.4230/LIPIcs.FUN.2022.20
Mirror Games Against an Open Book Player

Authors: Roey Magen and Moni Naor

Published in: LIPIcs, Volume 226, 11th International Conference on Fun with Algorithms (FUN 2022)

Abstract
Mirror games were invented by Garg and Schnieder (ITCS 2019). Alice and Bob take turns (with Alice playing first) in declaring numbers from the set {1,2, …, 2n}. If a player picks a number that was previously played, that player loses and the other player wins. If all numbers are declared without repetition, the result is a draw. Bob has a simple mirror strategy that assures he won't lose and requires no memory. On the other hand, Garg and Schenier showed that every deterministic Alice needs memory of size linear in n in order to secure a draw. Regarding probabilistic strategies, previous work showed that a model where Alice has access to a secret random perfect matching over {1,2, …, 2n} allows her to achieve a draw in the game w.p. a least 1-1/n and using only polylog bits of memory. We show that the requirement for secret bits is crucial: for an "open book" Alice with no secrets (Bob knows her memory but not future coin flips) and memory of at most n/4c bits for any c ≥ 2, there is a Bob that wins w.p. close to 1-{2^{-c/2}}.
Cite as

Roey Magen and Moni Naor. Mirror Games Against an Open Book Player. In 11th International Conference on Fun with Algorithms (FUN 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 226, pp. 20:1-20:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{magen_et_al:LIPIcs.FUN.2022.20,
  author =	{Magen, Roey and Naor, Moni},
  title =	{{Mirror Games Against an Open Book Player}},
  booktitle =	{11th International Conference on Fun with Algorithms (FUN 2022)},
  pages =	{20:1--20:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-232-7},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{226},
  editor =	{Fraigniaud, Pierre and Uno, Yushi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FUN.2022.20},
  URN =		{urn:nbn:de:0030-drops-159900},
  doi =		{10.4230/LIPIcs.FUN.2022.20},
  annote =	{Keywords: Mirror Games, Space Complexity, Eventown-Oddtown}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2022.107
Keep That Card in Mind: Card Guessing with Limited Memory

Authors: Boaz Menuhin and Moni Naor

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)

Abstract
A card guessing game is played between two players, Guesser and Dealer. At the beginning of the game, the Dealer holds a deck of n cards (labeled 1, ..., n). For n turns, the Dealer draws a card from the deck, the Guesser guesses which card was drawn, and then the card is discarded from the deck. The Guesser receives a point for each correctly guessed card. With perfect memory, a Guesser can keep track of all cards that were played so far and pick at random a card that has not appeared so far, yielding in expectation ln n correct guesses, regardless of how the Dealer arranges the deck. With no memory, the best a Guesser can do will result in a single guess in expectation. We consider the case of a memory bounded Guesser that has m < n memory bits. We show that the performance of such a memory bounded Guesser depends much on the behavior of the Dealer. In more detail, we show that there is a gap between the static case, where the Dealer draws cards from a properly shuffled deck or a prearranged one, and the adaptive case, where the Dealer draws cards thoughtfully, in an adversarial manner. Specifically: 1) We show a Guesser with O(log² n) memory bits that scores a near optimal result against any static Dealer. 2) We show that no Guesser with m bits of memory can score better than O(√m) correct guesses against a random Dealer, thus, no Guesser can score better than min {√m, ln n}, i.e., the above Guesser is optimal. 3) We show an efficient adaptive Dealer against which no Guesser with m memory bits can make more than ln m + 2 ln log n + O(1) correct guesses in expectation. These results are (almost) tight, and we prove them using compression arguments that harness the guessing strategy for encoding.
Cite as

Boaz Menuhin and Moni Naor. Keep That Card in Mind: Card Guessing with Limited Memory. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 107:1-107:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{menuhin_et_al:LIPIcs.ITCS.2022.107,
  author =	{Menuhin, Boaz and Naor, Moni},
  title =	{{Keep That Card in Mind: Card Guessing with Limited Memory}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{107:1--107:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.107},
  URN =		{urn:nbn:de:0030-drops-157039},
  doi =		{10.4230/LIPIcs.ITCS.2022.107},
  annote =	{Keywords: Adaptivity vs Non-adaptivity, Adversarial Robustness, Card Guessing, Compression Argument, Information Theory, Streaming Algorithms, Two Player Game}
}
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